#  Data Ingest
KnightABS.df <- read_sav("Gallup Source Data/Knight Foundation 2019-2020 ABS Public Release Data.SAV")
# sanity check
if (interactive()) {
  KnightABS.df %>% ExPanD(df=., export_nb_option=TRUE)
}
# Recode for R standards
## missing value codes
## value labels -> factors
kg.df <- KnightABS.df
kg.df %<>% to_factor(.,ordered=TRUE)
kg.df %<>% mutate(across(where(is.factor),function(x)fct_recode(x,NULL="No answer")))
kg.df %<>% mutate(across(where(is.factor),function(x)fct_recode(x,NULL="Undesignated")))
kg.df %<>% mutate(across(where(is.factor),function(x)fct_explicit_na(x,na_level="(Missing)")))
# to_factor drops var  lables -- restore these
var_label(kg.df)<-var_label(KnightABS.df)
# Cluster / of responses - factor analysis?
require(factoextra)
pca.res<-prcomp(kg.df %>% transmute(across(where(is.factor),as.numeric)),scale=TRUE,na.omit=TRUE)
In prcomp.default(kg.df %>% transmute(across(where(is.factor), as.numeric)), 
    scale = TRUE, na.omit = TRUE) :
 extra argument 㤼㸱na.omit㤼㸲 will be disregarded
 fviz_eig(pca.res)


 
 fviz_pca_var(pca.res, col.var = "contrib",  gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"), repel = TRUE )

NA
ggplotly(trustplot2)
Use of `map_df$x` is discouraged. Use `x` instead.Use of `map_df$y` is discouraged. Use `y` instead.Use of `map_df$group` is discouraged. Use `group` instead.`group_by_()` is deprecated as of dplyr 0.7.0.
Please use `group_by()` instead.
See vignette('programming') for more help
This warning is displayed once every 8 hours.
Call `lifecycle::last_warnings()` to see where this warning was generated.

Warning messages:
1: In readChar(file, size, TRUE) : truncating string with embedded nuls
2: In readChar(file, size, TRUE) : truncating string with embedded nuls
3: In readChar(file, size, TRUE) : truncating string with embedded nuls
# Multivariate predictors of overall trust in media (Q5)
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